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20 January 2005 Algorithm to estimate PAR at the ocean surface from GLI data
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Abstract
A simple, yet efficient and fairly accurate algorithm is presented to estimate photosynthetically available radiation (PAR) at the ocean surface from Global Imager (GLI) data. The algorithm utilizes plane-parallel radiation-transfer theory and separates the effects of the clear atmosphere and clouds. PAR is computed as the difference between the 400-700 nm solar flux incident at the top of the atmosphere (known) and reflected back to space by the atmosphere and surface (derived from GLI radiance), taking into account atmospheric absorption. Knowledge of pixel composition is not required, eliminating the need for cloud screening and arbitrary assumptions about sub-pixel cloudiness. For each GLI pixel, clear or cloudy, a daily PAR estimate is obtained. Diurnal changes in cloudiness are taken into account statistically, using a regional diurnal albedo climatology based on 5 years of ERBS data. The algorithm results are verified against other satellite estimates of PAR, the NCEP reanalysis product, and in-situ measurements from fixed buoys. Agreement is generally good between SeaWiFS and GLI estimates, with rms differences of 10 (28%), 5 (14%), and 3 (8.5%) Einstein/m2/day on daily, weekly, and monthly time scales. The rms differences between GLI and VISSR estimates and GLI and NCEP estimates are larger, i.e., 7.2 (15%) and 9 (30%) Einstein/m2/day, respectively, on monthly values. The comparison with buoy data also shows good agreement, with inaccuracies of 11.6 (28%), 10.3 (26%), and 7.1 (18%) Einstein/m2/day on daily, weekly, and monthly time scales. The good statistical performance makes the algorithm suitable for large-scale studies of aquatic photosynthesis.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Robert J. Frouin and Hiroshi Murakami "Algorithm to estimate PAR at the ocean surface from GLI data", Proc. SPIE 5656, Active and Passive Remote Sensing of the Oceans, (20 January 2005); https://doi.org/10.1117/12.582524
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